http://informahealthcare.com/jdt ISSN: 0954-6634 (print), 1471-1753 (electronic) J Dermatolog Treat, 2015; 26(1): 94–96 ! 2014 Informa Healthcare USA, Inc. DOI: 10.3109/09546634.2013.878447
REVIEW ARTICLE
Pharmacogenomics in dermatology: Taking patient treatment to the next level Jessica Schweitzer1,2 and Howard Maibach2 1
Western University of Health Sciences, Pomona, CA, USA and 2Department of Dermatology, UCSF, San Francisco, CA, USA
Abstract
Keywords
The notion of treating the patient, and not the particular disease, has been emphasized by physicians for some time. In the past decade, this idea advanced with the human genome project, and has been taken further with the advent of personalized dermatology, or using genetics to drive pharmacological treatment. For example, recent melanoma treatment trials focus entirely on the genetic makeup of the individual. Although some dermatological conditions such as melanoma are being targeted with gene-specific therapy, the idea of choosing a drug based on the genetic makeup to treat other dermatologic conditions might be relevant, since it may increase drug efficacy or decrease adverse drug events. This concept of pharmacogenomics could be applied throughout the field of dermatology. Online libraries have been developed to guide drug efficacy, dose prediction and adverse events. We provide a list of current systemic dermatologic drugs in which the pharmacokinetics and pharmacodynamics have been studied. It would be beneficial to guide patient treatment with these drugs, if we can better understand their pharmacogenomics.
Drug database, human genome project, metastatic melanoma treatment, mutational load, pathway mutational load, vemurafenib
Introduction The notion of treating the patient, and not the particular disease, has been emphasized by physicians for some time. In the past decade, this idea advanced with the human genome project (1), and has been taken further with the advent of personalized dermatology, or using genetics to drive pharmacological treatment. There is a vast array of online resources that provide information of this manner on many different dermatologic drugs.
Current databases Online libraries have been developed to guide drug efficacy, dose prediction and adverse events. Since the advent of the human genome project, institutions have developed online single-nucleotide polymorphism (SNP) libraries (2). In addition to gene mapping, the Pharmacogenomics Knowledge Base (PKB) consists of pharmacogenomic variants, along with the individual drug responses (3). In this database, one can determine a patient’s risk of an adverse drug event based on an individual’s genotype. PKB also has a haplotype database that involves combinations of SNPs. Another database, DrugBank, includes chemical and genetic information for 1350 FDA approved drugs (3). Additionally, the Polyphen tool was developed at Harvard to assess rare gene variants and predict their impact on downstream protein products (4). Using the databases previously mentioned, the pHap method was developed to estimate mutational load, which is the
Correspondence: Howard Maibach, Department of Dermatology, UCSF, San Francisco, CA 94143-0980, USA. Tel: (495) 673-9693. Fax: (495) 673-3533. E-mail: [email protected]
History Received 21 September 2013 Accepted 8 December 2013 Published online 19 February 2014
‘‘quantification of the total genetic deviation for all loci contained within or defined by an organizing genetic unit, such as the gene’’ (3). The mutational load is derived from using the population allele frequency from the HapMap project to measure the aberration from wild type alleles. In one study, the mutational load was used to help predict a patient’s necessary warfarin dose (5). Dudley et al. then proposed the concept of the pathway mutational load (PML), which sums each mutational load for each identifying gene involved in a drug pathway. They referenced databases including the KEGG DRUG database and PharmGKB that have information on the pharmacodynamics and pharmacokinetics of many drugs. This PML score can then be determined for a personal genome. Since these computations are preliminary, clinical trials should be conducted that monitor the responsiveness of the drug to correlate those findings with the PML score. Many resources have the potential to assist in applying pharmacogenomics in patient care. Since these analyses are still in silico (computer based), they should be supplemented with more validated tests until the approach is more clinically advanced (3).
Pharmacogenomics in metastatic melanoma treatment Just as the genomics of warfarin metabolism have the potential to assist with drug dose monitoring, genomics has already begun to drive treatment of some dermatological conditions, including metastatic melanoma. A simplified molecular pathway of genes involved in melanoma progression is illustrated (Figure 1). Different medications have been produced or are currently being studied to target specific genes in this pathway. For example, patients with metastatic melanoma who test positive for the V600E mutation can receive treatment with vemurafenib, a drug that inhibits the MAP kinase pathway by inhibiting BRAF (6). In addition to driving drug efficacy, screening a patient for
Pharmacogenomics in dermatology
DOI: 10.3109/09546634.2013.878447
the V600E mutation is advised prior to vemurafenib treatment because preclinical studies have shown that BRAF inhibitors can actually enhance MAPK expression in those with wild type BRAF and upstream RAS mutations (7). Moving forward in the sequence, trametinib inhibits MEK1 and MEK2 in those with the BRAF V600E or V600K mutations (8). A list of current therapeutic targets from previous studies or in current trials for metastatic melanoma is provided in Table 1 (9). Genes involved in melanoma can be patient specific. Other mutations that are found in only some patients with melanoma include neuroblastoma RAS viral oncogene homolog in 15–20% of patients, c-KIT in 28–39% of patients, CDK4 in 55%, of patients and GNAQ in uveal melanoma (10). There are also ongoing trials to develop drugs to target these mutations.
Figure 1. Simplified pathway of genetic targets in melanoma.
Table 1. Current targets and treatments in metastatic melanoma. Drug
Target to inhibit
Ipilimumab Nivolumab, Lambrolizumab Dabrafenib, Vemurafenib Lenvatinib Trametinib GSK1120212, AZD6244 molecules, currently in clinical trials Sorafenib, KIT
CTLA-4 PD-1 BRAF VEGFR1-3, FGFR1-4, RET, KIT, PDGFR MEK MEK Multikinase
Discussion: Expanding pharmacogenomics in dermatology In addition to melanoma, a newly FDA approved drug, vismodegib, has been used for metastatic basal cell carcinoma (11). Additionally, trials examining gefinitib and cetuximab to inhibit EGFR in non-melanoma skin cancers have shown that patient response is dependent on genotypic makeup. Trials report that cetuximab was ineffective and gefinitib less responsive in those with non-EGFR expression tumors (12). Other biomarkers that affect dermatologic treatment include dihydropyrimadine dehydrogenase (DPD) targeted by 5-flourouracil, and cevimeline, which is metabolized by the CYP2D6 enzyme assay (13). Hence, pharmacogenomics is being used in several ways to treat skin cancer and some other dermatologic conditions. We can apply the concept of gene targeting in oncologic treatments by personalizing drug therapy for many other dermatologic conditions. Table 2 lists systemic dermatologic drugs with known gene targets that have documented adverse affects or risks of drug toxicity. These drugs have been studied sufficiently to know the pharmacokinetics and pharmacodynamics of each (14,15,13,16). Since the underlying strategy of pharmacogenomics and personalized medicine is to improve drug efficacy and determine the risk of adverse effects, it would be ideal to drive treatment with all systemic dermatological drugs in this manner. This will require further clinical trials to corroborate the clinical accuracy with the information derived from these online drug databases.
Conclusion Catering dermatologic treatment to the individual person would provide many benefits. Current drug databases provide tools to predict the possibility of a patient’s adverse drug events, and also give pharmacokinetic and pharmacodynamic
Table 2. Systemic dermatologic drugs with either significant adverse effects or potential toxicity, all of which have reported gene targets.
Drugs for infectious diseases Antiviral Zidovudine Interferon Antifungal Ketoconazole Terbinafine Flucytosine Antibacterial Tetracycline Erythromycin Penicillin Amoxicillin-clavulanate Trimethoprim-Sulfamethoxazole Rifampin Ciprofloxacin Metronidazole Clindamycin Vancomycin Antimalarials Chloroquine Hydroxycholoroquine Quinacrine Primaquine
95
Immunomodulatory and antiproliferative agents Oral glucocorticosteroids Cytotoxic agents Azathioprine Cyclophosphamide Chlorambucil Mercaptopurine Fluorouracil Melphalan Vinblastine Hydroxyurea Bleomycin Mycophenolic acid Decarbazine Actinomycin D Procarbazine Vismodegib Gefinitib Cetuximab 5-flourouracil Cevimeline Immunomodulators Alefacept Efalizumab Etanercept Infliximab Adalimumab Ustekinumab
Other Antiandrogenic Drospirenone Ethinyl Estradiol Vasoactive and antiplatelet drugs Pentoxifylline Dipyridamole Other Methotrexate Cyclosporine Isotretinoin Acitretin Dapsone Phenytoin NSAIDs (i.e. naproxen) Etretinate
96
J. Schweitzer & H. Maibach
information about different drugs. We can increase drug efficacy by using this information to provide pharmacologic treatment based on the genetic makeup of an individual. While this strategy has not been developed enough to apply to all dermatological drugs, it presents an opportunity for the medical community to correlate clinical trials with the aforementioned databases, and to change the way we treat the patient, instead of the disease, in the future.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper. Internet Resources – pharmgkb.org – drugbank.ca – genetics.bwh.harvard.edu/pph2/ – http://www.ncbi.nlm.nih.gov/projects/genome/guide/human/index. shtml – http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacog enetics/ucm083378.htm
References 1. Rizzo A, Maibach H. Expression profiling to individualize dermatologic therapy. J Dermatol Treat. 2012;23:161–7. 2. Kitts A, Sherry S. The Single Nucleotide Polymorphism Database (dbSNP) of nucleotide sequence variation. In: McEntyre J, Ostell J, eds. The NCBI Handbook [Internet], Chapter 5. Bethesda (MD): National Center for Biotechnology Information (US), 2002. Available from http://www.ncbi.nlm.nih.gov/books/NBK21088/ [2002 Oct 9; Updated 2011 Feb 2]. 3. Dudley J, Karczewski J. Exploring Personal Genomics, 1st ed. Oxford: Oxford UP, 2013.
J Dermatolog Treat, 2015; 26(1): 94–96
4. Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nature Methods. 2010;7: 248–9. 5. Tatonetti NP, Dudley JT, Sagreiya H, et al. An integrative method for scoring candidate genes from association studies: application to warfarin dosing. BMC Bioinformatics. 2010;11:S9. 6. Sosman JA, Kim KB, Schuchter L, et al. Survival in BRAF V600mutant advanced melanoma treated with vemurafenib. N Engl J Med. 2012;366:707–14. 7. Hatzivassiliou G, Song K, Yen I, et al. RAF inhibitors prime wildtype RAF to activate the MAPK pathway and enhance growth. Nature. 2010;464:431–5. 8. Flaherty KT, Robert C. Improved survival with MEK inhibition in BRAF-mutated melanoma. N Engl J Med. 2012;367:104–14. 9. Hitt, E. Novel Agents Revolutionize Melanoma Treatment. New Jersey: OncLive, 2013. Available from http:// www.onclive.com/publications/obtn/2013/August-2013/Novel-AgentsRevolutionize-Melanoma-Treatment Last accessed Sept 19, 2013 10. National Cancer Institute: PDQÕ Melanoma Treatment. Bethesda, MD: National Cancer Institute. Date last modified 55/16/20134. Availablefrom http://cancer.gov/cancertopics/pdq/treatment/melanoma/HealthProfessional. Accessed October 9, 2013. 11. Henkin RI. Vismodegib in advanced basal cell carcinoma. N Engl J Med. 2012;367:970–1. 12. Grealy RE, Griffiths LY. Current status of pharmacogenomics testing for anti-tumor drug therapies. Mol Diag Theory. 2009;13: 65–72. 13. Greenfield NP, Maibach HI. Pharmacogenomic biomarkers in dermatologic drugs. J Dermatol Treat 2012. Epub ahead of print. 14. Wolverton SE, Jonathan KW. Systemic Drugs for Skin Diseases, 1st ed. Philadelphia: Saunders, 1991. 15. Katzung BG, Masters SU, Trevor AN, . Basic & Clinical Pharmacology, 12th ed. New York: McGraw-Hill Medical, 2011. 16. Wishart DS, Knox C, Guo AC, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34:D668–72.
Copyright of Journal of Dermatological Treatment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
REVIEW ARTICLE
Pharmacogenomics in dermatology: Taking patient treatment to the next level Jessica Schweitzer1,2 and Howard Maibach2 1
Western University of Health Sciences, Pomona, CA, USA and 2Department of Dermatology, UCSF, San Francisco, CA, USA
Abstract
Keywords
The notion of treating the patient, and not the particular disease, has been emphasized by physicians for some time. In the past decade, this idea advanced with the human genome project, and has been taken further with the advent of personalized dermatology, or using genetics to drive pharmacological treatment. For example, recent melanoma treatment trials focus entirely on the genetic makeup of the individual. Although some dermatological conditions such as melanoma are being targeted with gene-specific therapy, the idea of choosing a drug based on the genetic makeup to treat other dermatologic conditions might be relevant, since it may increase drug efficacy or decrease adverse drug events. This concept of pharmacogenomics could be applied throughout the field of dermatology. Online libraries have been developed to guide drug efficacy, dose prediction and adverse events. We provide a list of current systemic dermatologic drugs in which the pharmacokinetics and pharmacodynamics have been studied. It would be beneficial to guide patient treatment with these drugs, if we can better understand their pharmacogenomics.
Drug database, human genome project, metastatic melanoma treatment, mutational load, pathway mutational load, vemurafenib
Introduction The notion of treating the patient, and not the particular disease, has been emphasized by physicians for some time. In the past decade, this idea advanced with the human genome project (1), and has been taken further with the advent of personalized dermatology, or using genetics to drive pharmacological treatment. There is a vast array of online resources that provide information of this manner on many different dermatologic drugs.
Current databases Online libraries have been developed to guide drug efficacy, dose prediction and adverse events. Since the advent of the human genome project, institutions have developed online single-nucleotide polymorphism (SNP) libraries (2). In addition to gene mapping, the Pharmacogenomics Knowledge Base (PKB) consists of pharmacogenomic variants, along with the individual drug responses (3). In this database, one can determine a patient’s risk of an adverse drug event based on an individual’s genotype. PKB also has a haplotype database that involves combinations of SNPs. Another database, DrugBank, includes chemical and genetic information for 1350 FDA approved drugs (3). Additionally, the Polyphen tool was developed at Harvard to assess rare gene variants and predict their impact on downstream protein products (4). Using the databases previously mentioned, the pHap method was developed to estimate mutational load, which is the
Correspondence: Howard Maibach, Department of Dermatology, UCSF, San Francisco, CA 94143-0980, USA. Tel: (495) 673-9693. Fax: (495) 673-3533. E-mail: [email protected]
History Received 21 September 2013 Accepted 8 December 2013 Published online 19 February 2014
‘‘quantification of the total genetic deviation for all loci contained within or defined by an organizing genetic unit, such as the gene’’ (3). The mutational load is derived from using the population allele frequency from the HapMap project to measure the aberration from wild type alleles. In one study, the mutational load was used to help predict a patient’s necessary warfarin dose (5). Dudley et al. then proposed the concept of the pathway mutational load (PML), which sums each mutational load for each identifying gene involved in a drug pathway. They referenced databases including the KEGG DRUG database and PharmGKB that have information on the pharmacodynamics and pharmacokinetics of many drugs. This PML score can then be determined for a personal genome. Since these computations are preliminary, clinical trials should be conducted that monitor the responsiveness of the drug to correlate those findings with the PML score. Many resources have the potential to assist in applying pharmacogenomics in patient care. Since these analyses are still in silico (computer based), they should be supplemented with more validated tests until the approach is more clinically advanced (3).
Pharmacogenomics in metastatic melanoma treatment Just as the genomics of warfarin metabolism have the potential to assist with drug dose monitoring, genomics has already begun to drive treatment of some dermatological conditions, including metastatic melanoma. A simplified molecular pathway of genes involved in melanoma progression is illustrated (Figure 1). Different medications have been produced or are currently being studied to target specific genes in this pathway. For example, patients with metastatic melanoma who test positive for the V600E mutation can receive treatment with vemurafenib, a drug that inhibits the MAP kinase pathway by inhibiting BRAF (6). In addition to driving drug efficacy, screening a patient for
Pharmacogenomics in dermatology
DOI: 10.3109/09546634.2013.878447
the V600E mutation is advised prior to vemurafenib treatment because preclinical studies have shown that BRAF inhibitors can actually enhance MAPK expression in those with wild type BRAF and upstream RAS mutations (7). Moving forward in the sequence, trametinib inhibits MEK1 and MEK2 in those with the BRAF V600E or V600K mutations (8). A list of current therapeutic targets from previous studies or in current trials for metastatic melanoma is provided in Table 1 (9). Genes involved in melanoma can be patient specific. Other mutations that are found in only some patients with melanoma include neuroblastoma RAS viral oncogene homolog in 15–20% of patients, c-KIT in 28–39% of patients, CDK4 in 55%, of patients and GNAQ in uveal melanoma (10). There are also ongoing trials to develop drugs to target these mutations.
Figure 1. Simplified pathway of genetic targets in melanoma.
Table 1. Current targets and treatments in metastatic melanoma. Drug
Target to inhibit
Ipilimumab Nivolumab, Lambrolizumab Dabrafenib, Vemurafenib Lenvatinib Trametinib GSK1120212, AZD6244 molecules, currently in clinical trials Sorafenib, KIT
CTLA-4 PD-1 BRAF VEGFR1-3, FGFR1-4, RET, KIT, PDGFR MEK MEK Multikinase
Discussion: Expanding pharmacogenomics in dermatology In addition to melanoma, a newly FDA approved drug, vismodegib, has been used for metastatic basal cell carcinoma (11). Additionally, trials examining gefinitib and cetuximab to inhibit EGFR in non-melanoma skin cancers have shown that patient response is dependent on genotypic makeup. Trials report that cetuximab was ineffective and gefinitib less responsive in those with non-EGFR expression tumors (12). Other biomarkers that affect dermatologic treatment include dihydropyrimadine dehydrogenase (DPD) targeted by 5-flourouracil, and cevimeline, which is metabolized by the CYP2D6 enzyme assay (13). Hence, pharmacogenomics is being used in several ways to treat skin cancer and some other dermatologic conditions. We can apply the concept of gene targeting in oncologic treatments by personalizing drug therapy for many other dermatologic conditions. Table 2 lists systemic dermatologic drugs with known gene targets that have documented adverse affects or risks of drug toxicity. These drugs have been studied sufficiently to know the pharmacokinetics and pharmacodynamics of each (14,15,13,16). Since the underlying strategy of pharmacogenomics and personalized medicine is to improve drug efficacy and determine the risk of adverse effects, it would be ideal to drive treatment with all systemic dermatological drugs in this manner. This will require further clinical trials to corroborate the clinical accuracy with the information derived from these online drug databases.
Conclusion Catering dermatologic treatment to the individual person would provide many benefits. Current drug databases provide tools to predict the possibility of a patient’s adverse drug events, and also give pharmacokinetic and pharmacodynamic
Table 2. Systemic dermatologic drugs with either significant adverse effects or potential toxicity, all of which have reported gene targets.
Drugs for infectious diseases Antiviral Zidovudine Interferon Antifungal Ketoconazole Terbinafine Flucytosine Antibacterial Tetracycline Erythromycin Penicillin Amoxicillin-clavulanate Trimethoprim-Sulfamethoxazole Rifampin Ciprofloxacin Metronidazole Clindamycin Vancomycin Antimalarials Chloroquine Hydroxycholoroquine Quinacrine Primaquine
95
Immunomodulatory and antiproliferative agents Oral glucocorticosteroids Cytotoxic agents Azathioprine Cyclophosphamide Chlorambucil Mercaptopurine Fluorouracil Melphalan Vinblastine Hydroxyurea Bleomycin Mycophenolic acid Decarbazine Actinomycin D Procarbazine Vismodegib Gefinitib Cetuximab 5-flourouracil Cevimeline Immunomodulators Alefacept Efalizumab Etanercept Infliximab Adalimumab Ustekinumab
Other Antiandrogenic Drospirenone Ethinyl Estradiol Vasoactive and antiplatelet drugs Pentoxifylline Dipyridamole Other Methotrexate Cyclosporine Isotretinoin Acitretin Dapsone Phenytoin NSAIDs (i.e. naproxen) Etretinate
96
J. Schweitzer & H. Maibach
information about different drugs. We can increase drug efficacy by using this information to provide pharmacologic treatment based on the genetic makeup of an individual. While this strategy has not been developed enough to apply to all dermatological drugs, it presents an opportunity for the medical community to correlate clinical trials with the aforementioned databases, and to change the way we treat the patient, instead of the disease, in the future.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper. Internet Resources – pharmgkb.org – drugbank.ca – genetics.bwh.harvard.edu/pph2/ – http://www.ncbi.nlm.nih.gov/projects/genome/guide/human/index. shtml – http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacog enetics/ucm083378.htm
References 1. Rizzo A, Maibach H. Expression profiling to individualize dermatologic therapy. J Dermatol Treat. 2012;23:161–7. 2. Kitts A, Sherry S. The Single Nucleotide Polymorphism Database (dbSNP) of nucleotide sequence variation. In: McEntyre J, Ostell J, eds. The NCBI Handbook [Internet], Chapter 5. Bethesda (MD): National Center for Biotechnology Information (US), 2002. Available from http://www.ncbi.nlm.nih.gov/books/NBK21088/ [2002 Oct 9; Updated 2011 Feb 2]. 3. Dudley J, Karczewski J. Exploring Personal Genomics, 1st ed. Oxford: Oxford UP, 2013.
J Dermatolog Treat, 2015; 26(1): 94–96
4. Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nature Methods. 2010;7: 248–9. 5. Tatonetti NP, Dudley JT, Sagreiya H, et al. An integrative method for scoring candidate genes from association studies: application to warfarin dosing. BMC Bioinformatics. 2010;11:S9. 6. Sosman JA, Kim KB, Schuchter L, et al. Survival in BRAF V600mutant advanced melanoma treated with vemurafenib. N Engl J Med. 2012;366:707–14. 7. Hatzivassiliou G, Song K, Yen I, et al. RAF inhibitors prime wildtype RAF to activate the MAPK pathway and enhance growth. Nature. 2010;464:431–5. 8. Flaherty KT, Robert C. Improved survival with MEK inhibition in BRAF-mutated melanoma. N Engl J Med. 2012;367:104–14. 9. Hitt, E. Novel Agents Revolutionize Melanoma Treatment. New Jersey: OncLive, 2013. Available from http:// www.onclive.com/publications/obtn/2013/August-2013/Novel-AgentsRevolutionize-Melanoma-Treatment Last accessed Sept 19, 2013 10. National Cancer Institute: PDQÕ Melanoma Treatment. Bethesda, MD: National Cancer Institute. Date last modified 55/16/20134. Availablefrom http://cancer.gov/cancertopics/pdq/treatment/melanoma/HealthProfessional. Accessed October 9, 2013. 11. Henkin RI. Vismodegib in advanced basal cell carcinoma. N Engl J Med. 2012;367:970–1. 12. Grealy RE, Griffiths LY. Current status of pharmacogenomics testing for anti-tumor drug therapies. Mol Diag Theory. 2009;13: 65–72. 13. Greenfield NP, Maibach HI. Pharmacogenomic biomarkers in dermatologic drugs. J Dermatol Treat 2012. Epub ahead of print. 14. Wolverton SE, Jonathan KW. Systemic Drugs for Skin Diseases, 1st ed. Philadelphia: Saunders, 1991. 15. Katzung BG, Masters SU, Trevor AN, . Basic & Clinical Pharmacology, 12th ed. New York: McGraw-Hill Medical, 2011. 16. Wishart DS, Knox C, Guo AC, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34:D668–72.
Copyright of Journal of Dermatological Treatment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
